1. Field of the Invention
The present invention relates to a magnetic resonance imaging (MRI) technique, and more particularly, to an image processing technique of a reconstructed image.
2. Description of Prior Art
A magnetic resonance imaging apparatus (hereinafter, referred to as an MRI apparatus) is a diagnostic imaging device for medical use, which applies a radio frequency magnetic field and a gradient magnetic field to a subject placed in a static magnetic field, measures a signal generated from the subject by magnetic resonance, and forms an image from the signal.
In the MRI apparatus, in general, a slice gradient magnetic field for specifying an imaging section is applied, and simultaneously, an excitation pulse for exciting magnetization in the imaging section (radio frequency magnetic field pulse) is applied. Thus, a magnetic resonance signal (echo) generated in convergence of the excited magnetization is obtained. In this process, in order to impart three-dimensional positional information to the magnetization, the slice gradient magnetic field, a phase encoding gradient magnetic field and a read-out gradient magnetic field that are perpendicular to each other on the imaging section are applied during the period from the excitation to the obtainment of the echo. The measured echoes are arranged in a k-space having a kx axis, a ky axis, and a kz axis, and are subjected to inverse Fourier transform to perform image reconstruction.
Each pixel value of the reconstructed image forms a complex number including an absolute value and a declination (phase). A gray-scale image in which the pixel values are the absolute values (absolute image) is an image that reflects the density of protons (hydrogen nuclei) or relaxation times (T1 and T2), which is advantageous for depiction of a tissue structure. On the other hand, a gray-scale image in which the pixel values are the phase values (phase image) is an image that reflects a magnetic field change due to unevenness of the static magnetic field, a susceptibility difference between biological tissues, or the like.
In recent years, a quantitative susceptibility mapping (QSM) technique that uses the fact that a phase image reflects a susceptibility difference between tissues and estimates susceptibility distribution in a living body from the phase image has been proposed. An image obtained by approximately estimating the susceptibility of each pixel from the phase image based on the relationship between the phase and the susceptibility and using the estimated susceptibility as the pixel value is referred to as a susceptibility map. It is known that the susceptibility of the living body is changed according to the amount of iron or the amount of oxygen in a vein. The change of the susceptibility provides information useful for diagnosis of neurodegenerative diseases or apoplexy.
In the QSM, the susceptibility distribution is estimated from phase distribution. Here, since the phase distribution is calculated by spatially convolution-integrating the susceptibility distribution, the phase is changed in the vicinity of a region where the change of the susceptibility occurs according to the direction or intensity of the static magnetic field. Accordingly, when the susceptibility distribution is calculated from the phase distribution, the inverse problem of the convolution integration arises, which makes it difficult to simply obtain a solution.
In general, the susceptibility distribution is calculated from the phase distribution using the least squares method. Here, an error function is introduced, and a value that minimizes the error function is used as a solution.
The phase varies in a region where the signal-to-noise (SN) ratio of the image is low. Further, in a region where a tissue having a size smaller than 1 voxel exists, an error occurs in the phase value due to a partial volume effect. Hereinafter, the variation of the phase or the error of the phase value is referred to as phase variation. The phase variation decreases the calculation accuracy of the susceptibility map. Further, a streaking artifact occurs in the vicinity of a region where the phase variation is large.
In order to enhance the estimation accuracy of the susceptibility map to be calculated to reduce the artifact, weighting is performed for the error function according to the degree of the variation at each region in the phase image. The weighting is performed so that a weight of the region where the phase variation is large becomes small. An image in which the pixel value of each pixel is a weighting factor for the weighting of the error function used for the susceptibility calculation is referred to as a weighting image.
In the prior art, an absolute image is used as the weighting image (see Rochefort L, et al., “Quantitative Susceptibility Map Reconstruction from MR Phase Data Using Baysian Regularization: Validation and Application to Brain Imaging”, Magnetic Resonance in Medicine, 2010, Vol. 63, No. 1, pp. 194 to 206. (Non-patent document 1)). In a region where the pixel value of the absolute image is small, there is a tendency that the SN ratio of the image is small and the phase variation is large. Thus, by using a weight proportional to the pixel value of the absolute image, it is possible to decrease the weight of the region where the phase variation is large.